Conditional safety margins for less conservative peak local SAR assessment: A probabilistic approach

• Published in: Magnetic resonance in medicine Vol. 84; no. 6; pp. 3379 - 3395
• Main Authors: Meliadò, Ettore Flavio, Sbrizzi, Alessandro, van den Berg, Cornelis A. T., Steensma, Bart R., Luijten, Peter R., Raaijmakers, Alexander J. E.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2020; John Wiley and Sons Inc
• Subjects: Computer Simulation; Conditional probability; Databases, Factual; Dipoles; Full Papers—Computer Processing and Modeling; Machine learning; Magnetic Resonance Imaging; Male; Mathematical analysis; parallel transmit; Probabilistic methods; Probability density functions; Prostate; Safety; safety factor; Safety factors; Safety margins; SAR model library; SAR model selection; specific absorption rate; Statistical analysis; subject‐specific local SAR assessment; subject-specific local SAR assessment; specific absorption rate; SAR model library; SAR model selection; parallel transmit; safety factor
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.28335

Purpose The introduction of a linear safety factor to address peak local specific absorption rate (pSAR10g) uncertainties (eg, intersubject variation, modeling inaccuracies) bears one considerable drawback: It often results in over‐conservative scanning constraints. We present a more efficient approach to define a variable safety margin based on the conditional probability density function of the effectively obtained pSAR10g value, given the estimated pSAR10g value. Methods The conditional probability density function can be estimated from previously simulated data. A representative set of true and estimated pSAR10g samples was generated by means of our database of 23 subject‐specific models with an 8‐fractionated dipole array for prostate imaging at 7 T. The conditional probability density function was calculated for each possible estimated pSAR10g value and used to determine the corresponding safety margin with an arbitrary low probability of underestimation. This approach was applied to five state‐of‐the‐art local SAR estimation methods, namely: (1) using just the generic body model “Duke”; (2) using our model library to assess the maximum pSAR10g value over all models; (3) using the most representative “local SAR model”; (4) using the five most representative local SAR models; and (5) using a recently developed deep learning–based method. Results Compared with the more conventional safety factor, the conditional safety‐margin approach results in lower (up to 30%) mean overestimation for all investigated local SAR estimation methods. Conclusion The proposed probabilistic approach for pSAR10g correction allows more accurate local SAR assessment with much lower overestimation, while a predefined level of underestimation is accepted (eg, 0.1%).